A general-purpose framework for parallel processing of large-scale LiDAR data  

作　　者：Zhenlong Li Michael E.Hodgson Wenwen Li 

机构地区：[1]Department of Geography,University of South Carolina,Columbia,SC,USA [2]School of Geographical Sciences and Urban Planning,Arizona State University,Tempe,AZ,USA

出　　处：《International Journal of Digital Earth》2018年第1期26-47,共22页国际数字地球学报（英文）

基　　金：This study was funded by University of South Carolina through the ASPIRE(Advanced Support for Innovative Research Excellence)program[13540-16-41796];Additional funding was provided by the South Carolina Department of Transportation under contract to the University of South Carolina[SPR#707 or USC 13540FB11];USGS[G15AC00085];NSF-BCS[1455349].

摘　　要：Light detection and ranging(LiDAR)data are essential for scientific discoveries such as Earth and ecological sciences,environmental applications,and responding to natural disasters.While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands.Efficiently storing,managing,and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications.However,handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity.To tackle such challenges,we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle‘big’LiDAR data collections.The contributions of this research were(1)a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system,(2)two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks,and(3)by coupling existing LiDAR processing tools with Hadoop,a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application.A proof-of-concept prototype is presented here to demonstrate the feasibility,performance,and scalability of the proposed framework.

关 键 词：Big data online geoprocessing Hadoop MapReduce spatial decomposition LAStools PARALLEL 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]